{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']= ['SimHei']\n",
    "plt.rcParams[ 'axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def ONE():\n",
    "    url = 'https://www.gairuo.com/file/data/dataset/iris.data'\n",
    "    df = pd.read_csv(url)\n",
    "    fig, axes = plt.subplots(1,2)\n",
    "    df['sepal_length'].plot.hist(ax = axes[0])\n",
    "    df.plot.scatter(x='sepal_length',y = 'sepal_width',ax = axes[1])\n",
    "    plt.show()\n",
    "ONE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def TWO():\n",
    "    gdp =pd.read_csv('gdp.csv').set_index('province')\n",
    "    four = gdp.iloc[[0,8,9,18]]\n",
    "    fig, axes = plt.subplots(2,2)\n",
    "    four.T.sort_index().plot.line(ax = axes[0][0])\n",
    "    four['2016y'].plot.bar(rot=0,ax = axes[0][1])\n",
    "    four.T.sum().plot.pie(ax = axes[1][0])\n",
    "    plt.show()\n",
    "TWO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L9184qFEfGCr49F9fKVXBbrUwuEvTc9gnxth59NKhPHpp8NMjq6bToRullIpwGuiVUirCaaBXSqkIp4FeKaUinAZ6FXGeeuopDh48GO5mKNViaKBXESc1NZXbb7+dSy65hPfee4+ysrJwN0mpsNJAryLOzTffzJw5c7j//vv5wx/+wODBg/nzn/8c7mYpFTYa6FXEefLJJznzzDN59dVXefnll9m9ezf/+9//wt0spcJGF0ypiJOamso333xDQkJCRdnnn38exhYpFV5B79GLSJKIfCUi34jIxyISJSKvi8gyEXkk2PUpVd0tt9xSJcgDJCcnB7y/iCSLyJcislpEXvGX1TiH9bxWrUWDgV5EXm/kMX8CPGOMOR84DFwHWI0x44C+IjKg8c1UKnAjR4482UPcCMw0xqQDiSJyP9XOYRGZWr3sZCtVKlQC6dGLiJwW6AGNMS8ZY+b6n3YCbgBm+59/A4yvpYLp/t7T6uzs7ECrUqpWt9xyCy+88MLJHCIHGCYi7YGeQB9qnsMTaylTqkUKZIw+CpgnIl8DJYAxxtza0E4iMg5IBvYCB/zFucDo6tsaY2YAMwDS09Nr3qJGqUb49NNPOXToEO+99x6xsbGICN99911jDrEY+BHwa2ALvvdA9XM4vpayGkRkOjAdIC0trbF/ilJBEUig/73/J2AikgK8CFwF3AvE+l9KQGf6qBCbP3/+yR7iUeAXxphCEbkX+Cvwqv+14+dwMQGc19qJUS1Bg4HeGJMpIslAN3w9lyP1bS8iUcAHwEP+fTPwfa1dDowAtp10q5Wqh8fjYc2aNRULpQ4cOMCPf/zjxhwiGThVRJYDpwN/o+Y5vL+WMqVapAYDvYg8AFwJxAFPAhcAN9Wzy234vsb+XkR+D/wbuFFEugEXAWecbKOVqs+0adNITExkz549dOvWjby8vMYG+ifwnbe9gGXAs8CiauewqaVMqRYpkGGUS40xZwA5xpiZQN/6NjbGvGyMSTbGTPT/vIXvwtVyYJIxpuCkW61UPY4dO8a///1vOnfuzPvvv9/oFAjGmJXGmKHGmARjzHnGmEKqncO1lQX5z1AqaAIJ9IUichMQIyLnAPmNrcQYk2eMmW2MOdzYfZVqrLS0NGbPnk10dDRPPPEEhYWFJ33M2s7h1nZev71sL+f8fT7vLMsMd1NUMwsk0N8CjALygMvxDc0o1WK98847TJkyhZdeeonU1FRmz57d8E5twGNzNpOZU8qf5mxq0v6ZOSW88v0uPll7AK9Xryu3JnWO0YtI5blgz1Z6HB265ih18iwWC506dQLg1lsbnAncZqT3TmZNZh5jeqU0et99OaX86IXFlLs82KwWFu3I5h/XjAx+I1VI1Hcx9k/+30OARGAdMBwoB9JD2yylVLDN/NkZ7MstJS0lrtH7zt1yBKfbg8trcHk9fLruoAb6VqTOQG+M+SmAiMwFzjTGeEXEim8VoFItzqRJkxCRKmXGmKYsmIpIVovQp2N8k/btlhSDzWrB6fEA0DGhaV/sPV7D28v2sj4rn9P7dOC6sT1r/J+p4AtkwVQc8CMR2QAM9T9XqsUJwkKpNmHPsRKW786hfayd805JxWZt+FLdhcO6sGx3DrNXZ9EhPppXbhzTpLof+WQjn6w9SJnLw9ebjrAvr5QHLhzcpGOpwAUS6H8C3A/cAWTiy12jlGqFlu46xm1vrgZ833SGdG3H+9PPaDDYiwiPXT6Mxy4fdlL1Hw/yAGUuD7NXZWmgbwYNfpQbY/YaY+4wxlxkjPmFMWZXczRMqab67W9/W+X5kiVLdOaN30P/3UiZy0OZy0up08OWQ4V8vanexe5BlRBdtW+ZFGtvtrrbslCkKVYqrPbu3Ut6ejrPPfccAM8++yzvvfdeeBvVQhSUuao89xpDXqmz2er/xzUjiLVbSYy2ER9l5e9XD2+2utuyQIZuREROM8asCnlrlAqCgwcPsnr1asaNG8c999xDbm4udrv2HAEmDerMlxsPUe72VpSd0bdDs9V/9sBOLH5gEvtyS+nTMZ72cVHNVndbFrI0xUqFS9++fbnggguIiori+eefZ+fOnfTr1y/czWoRnph6Kl5jmL/1KIkxdh6feir9Oyc0vGMQdUiIpkMTZ+2opglJmmKlwundd98lLy+PuLg4Vq5cSUZGBqtW6RdSgBi7leevGxXuZqhmFkig71VLmSbLUC3a8XvETpgwAYCLL744nM0JC2MMa/bl8d6KfRzMd5AQY+OS4V25cFgXom3WcDdPNaNAAv0k/+9Y4DxgB7AwZC1S6iSNHDmSdevWhbsZYXUgv4xb3ljJgfwyylwejD81zdKdx3jk4x944fpRTBrUObyNVM0mkOmVf/L/PAiMBTQdq2rRgnDP2FYtu6icy/65mN3ZJZQ6TwR5gBKnh6JyN798N4OF27MpKHPxUcZ+Zq/K4mihI3yNDlBWbil3zlzDqMe+YfI/FvBhRhbGaIK1hgRy45HKyc06AXpVS7VoQbhnbKv21P+2UlDqwlNPAHS4vNzzn7VYrRZKyt0YAzar8PEdZzX7xdlA5RSXc+mLiyl0uPAayCt18YdPNpFT7OT2czQs1SeQoZsVwGZgH77bCfYPaYuUOkltORVCkcPFnA0HcQeQRrjQ4cIY8Pg3FTc8NmcTb992eohb2TSzVu6jzOWh8p9W5vLw4nc7uXV8H+wBpHJoqwIJ9JnAzwAPIPhuoaZUi/XYY4/VKPu///u/MLSk+W3YX4DdYsGBt8Ft3dU2MQaOFJY3qd5DBWX85v11ZBeV88CFgzl/aJcmHac+PxwsrDL//ziXx0t+qYtOiTplsy6BBPrDwDx8Af94oJ8cykYpdTLOOeccAMrKyvjqq6/wehsOepGi3O3xvUsDZLUIHn8XOcZuYeKgTk2q997317NyTy5eA3fNWsuKh89tcDGUMYZp/1rGnmMlfHrnWfRsIH3yyB5JLNh2FIer6v9ntM1CcpwuiKtPIIHeDpxqjCkNdWOUCobjgR7gwgsv5K677gpja5pXt/axuD2Bfem2W4URPdqzLisfA1w4tAv3nT+oSfVmF5dXGVIpcrgbDPROj5f1WflYRNiVXdxgoL9ubBqvLd6D2+OqGJqKtVu57/xBAWXgbMsCCfSpwCoRqch8ZIypt0cvIqnAh8aYCSJiA3b7fwDuMsZsbGqDlWrI22+/XfH46NGjbNiwIYytaV6DUhPpkhTDnmMlDW5rEeGf148mtV00xoDF0vS88A9dNJg7Zq4BfB8YPZJjG9wn2mbl/dvHcSC/jHMGNvxNon1cFJ/fNYFn521n0fZsOiREc+ekflw4rGuT291WNBjojTGNupuUiCQDbwHH73AwHJhljHmg8c1TqvHcbjfFxcV4PB66d+/OrFmzwt2kZiMi3Hf+QH73wYaKdMC1ibZZOHdwZ7okxfj3O7l6zx2SysqHp1DsdNMtKSbgm4mM6ZXMmF7JAdfTJSmGJ6/SRGiNFYrvOx7gWqDQ//wM4BIRWSkir/t7+EqFzNy5c9m8eTPx8fEsWbKEe+65J9xNalaXDO/GT8/qTay99tWvMXYLg1ITefqaEUGtNynOTvf2sXrHqBYo6EHXGFMIVP7PXgVMMcYcEpG3gYuBzyrvIyLTgekAaWmVp+0r1XiHDx+u0oufOHFi+BoTJvdfOJgxvZJ54dsdbD1chN1qweM1JMbY+PmEvtx0Zi9Ng9CGNEfveoMx5vicrdXAgOobGGNmADMA0tPTdfqmapKFC32ZOdq1a8df/vIXTj/9dDIyMoiPb9p9Ulu7c4ekcu6QVI4UOsguKicuykrvDvEnNRavWqfmCPTviMhfgR+AK4DHm6FO1QYdXyg1evRoPB4PS5cuBWDs2LHhbFbYpbaLIbVdTLibocKoOQL9Y8B7+Gb3fmaMmdcMdao26NFHHw13E1otYwwujyHKptMUI1HIAr0xZqL/9w/4Zt4opULA4fLw5883s3jnMXokx/LElcNJ61D/nPTq/jRnM28u3cu8e89psbluVNPpx7dSrdxvP1jPhxn7ycwpZdmuHKa+vISScnejjtEhPoqU+CiitUcfkfR/ValWbt6WIxU5YLzGl5ly6+HCBvaq6q5zB7DmD+fVujp1zb48Lnh2IeOf/I7Zq7OC0mbVvHROu1KtXFyUDYfLWfHc7fXSLiY4uV9Kyt3c9PpKiv3fEB79dBMDOicwKi3wRU4q/LRHr1Qr9/iVpxJjtxBrtxIXZeWS4d0YkJoYlGMfKiircWOPbYeLgnJs1Xy0R69UC5aVW8qCbUc5vW8HBtYRvC8c1oU5ncazdl8+XdvHML5/x6DV3yM5jhi7tVIeeMPoRqQsUMFzrLiccreX7u0bziNUnQZ6pVqoUqebS15cjMPlwSLCd789h65Jtb/JB6QmBq0XX1mM3co/rx/F019vx2sM950/qM4PHBU6JeVuJv59AS6Ply9+PZ7+nRv3f6CBXqkWKqfYicPlodztJS7Kyr6c0joDfSgUOVzcOXMtK/bkEGWz4HR7mbFwNyPT2pMQraGjuZ3IKtP4lc06Rq9UC9UjOZYfDe+KRWB0WvugDZnsOVbCeyv2selgQb3b3T1rHct3H6Pc7aXI4abc7WX5nhzunrW2Yhuv17Bqb26Dx1InJz7axve/m8SC301s0joH/VhWqoUSEZ65ZiT/uHpE0DJC7sou5tIXF2MMGAzv3HY6p/VOqbHd4QIHS3Ydw1ntJiZOt5fFO49xpNBBarsYfjN7HXM3H8EYuO/8gfxsQt+gtFPVlBJf/41c6qM9eqXqICIvicil/sevi8gyEXmk0us1ykLUjqAda+nOY3i8hjKXB4fLy9zNR2rdbn9eaZ3pEKJsFvbn+W44N2f9QUqdHspcHt5dnhm0dqrg0kCvVC1EZALQxRgzR0SmAlZjzDigr4gMqK0srA0O0PAe7SvGemPt1lp78wC9OsTjrOVG3ODr1ffq4MsIOrhLO+xWIcZmqfNYKvx06EapakTEDrwKfCkilwMTgdn+l78BxgOjainbUcuxWtS9Fkb0bM8bt5zGvM1HOL1vB847JbXW7TolRnP+0FTmbjqCo1LAj7FZOH9oFzomRAMw6+dnMHNFJgkxNq47Lfx/n6qdBnqlaroJ2Aw8BdwF3Am87n8tFxiN71aZB6qV1dAS77VwZr+OnNmv4bn2f582gocsG/ly4yHsVgsuj5eLT+3K41NPrdgmKc7OHZP6h7K5Kgg00CtV0yhghjHmsIi8C5wJHJ/XmIBvyLO4lrKIEmO38uy1I3n00lM4kF9G9/axtI9r+gXB48rdHv702Wa+3XqEzokx/P3q4Qzu0i4ILVZ1ibiTU6kg2Akcnz6SDvTGNzQDMALYC2TUUhaR2sdFMbRbUlCCPMBjczbz3zX7OVJYzsYDBVz7ynIKHa6gHFvVTnv0StX0OvCGiFwH2PGN0X8mIt2Ai/Dd8N4Ai6qVqQDM33q0yri/1xi2HS7Si7khpD16paoxxhQZY642xpxtjBlnjMnEF+yXA5OMMQXGmMLqZeFrcevSJSmmytpOl9tLaqLe6jCUNNArFQBjTJ4xZrYx5nB9ZeqELzce5NFPf+BgflmV8qemDad9nJ2EaBvRNgu/mjyg0XfEUo2jQzdKqaDLyi3lN++vx+XxsuVwEbNvH1fxWv/OiSx+YDI7jhbTMSGKHska5ENNA71SKuii7RYsIkTZLCTH1bwJSny0jZE92zd/w9ooDfRKqaDrnBjDx3eeyaYDhVx8atdwN6fNa1OB3u3xsn5/Ph4vjOiZRLTN2uRjGWPweA02q17mUK3D15sO8/6qLKJtFm4/p1/Ie9SDu7TT+fEtREgCvYikAh8aYyb4n78OnAJ8YYz5SyjqbEiZ08PVryxlT3YJCHRNiuXjO84ksQn31vxk7QEe+GgDLo+XqaN78NRVw7FYgpd4Sqlg+2/Gfn7/yQ+UuTwALNiWzazpZ+jwSRsR9O6oiCQDb+FbIk5LSf701tK97DhSTInTQ0m5h305Jbw0f1ejj3O0yMGDH22g3O3Fa+DLjYf4dP2BhndUKoye+3ZHRZAHKHN5eOX7xp//qnUKxbiDB7gWKPQ/n0jN5E9ViMh0EVktIquzs7ND0CRf2tXySos0nB7DPn+q1cY4WlheZbjG4fKwP7esnj2UCj+3p2YmyvI6slMel1vi5C+fb+aNxbvxeltEmh7VREEP9MaYwmqLR6onf6qRLs8YM8MYk26MSe/UqVOwmwTA2QM7EWs/MSYfa7cyeVDnRh+nf+cE4qKsHB+pibJZOHtgaNqsVLBMG9OTWPuJt3us3cp1p/Wsd5/ffbCeN5fu5e9fb+Oz9QdD3UQVQs1xMbZFJH86f2gX7jt/IP+cvxNj4LbxfZg6unujjxNjt/LJnWfx96+3UVDm4rbxfRih45yqhbtnim/E9IOMLOxWC/ecO4Dzh3apdx+312CMAQS39uhbNfH9R4bgwCILjDETReQmoLMx5mkR+ROwzRjzXl37paenm9WrV4ekTUqJSIYxJj0cdbe2c/tooYN/zN1Ot6QYfjV5AFadcNCi1XduN0eP/hM0+ZNSrU7ndjE8edXwcDdDBUHIhlGMMRP9vzX5k1JKhVGzLJgyxuRxYuaNqoPXa/hwzX6yi8q5Jr0nnRKjw90kpVQEaFMrY0PNGMOafXmUOb2k904mxt64lbfPzN3G64v34PIYZq3cx8LfTdKFWEqpk6aBPkiMMUx/J4MlO48hAh3io5lz13iSYgNfebtsdy5lLt/c5sMFDkqc7iat3FVKqco0UUuQZGTmsWTnMUr9K28PFziYtXJfo47xk9PTiLFbiI+yMrZPCgnR+jmsGqfI4SK3xNmofd5Zlsn4J7/j47W6wjtSaSQJklKnh8qjLG6vl5Jyd6OOMXV0D4Z2S+JYcTlj+6QgosM2KnBlTg8TnpyPw+Xhy7sn0LdTQkD7PTdvOzklTp6bu50rRzV+bYlq+TTQ+2XllrLlkC9rw8DURHp3jG/U/n07xmO1CBYBr/GtPLyiCW+aQV0SGURio/dTymMMTo8XAzhrSXlQlzsm9eOV73dz56R+oWucCqs2H+jXZeXz1y82s2F/AXarBcH3JhnStR0PXzyEsX0avmHx0SIHl/2/JZQ6PRgDNovw96tH0C/AHlVle46VMGPhbgBuP7tvoz9wVNuVEG3j2/vOodTpoXeHeN5ftQ+71cKVo7rX++3wtvF9uW1832ZsqWpubTrQL9h2lF++m1FxAbRykqd1Wfnc9MYKnrlmBBef2q3e47y9NJPCMlfFMnG31zdrZmyfFB79dBM7jhYxOi2ZRy45hYRoG16v4ZWFu1ixO5erxnTn0hG+nv/RIgeX/XMxxQ7fkM/nGw7y3X0TgzrN8ptNh7nn/XUkxtj4z/Rx9NEPkojSNSmW/FInv5yZwfdbj2KxCFaLcPlIHZJpy9rsxdic4nJ++e6aiiBfG4fLy2/eX8f+BrJcFpW7auQCKSxzcdXLS/l602G2Hynmv2sO8NN/r8QY34fAC9/uZMH2bO7/cCMZmXkArNidi9drMOD7MbByT+7J/qlV/OWLLZQ6PRwtLOf1RbuDemzVMny89gDfbDriPyeFEGU5Ua1Imw30s1buI5A8P+Vuw3WvLK/3wuolw7sRUy0z4Pj+HTlWVF7xAeD0eFmXlU9BmYuth4tO5AYX2J1dDEBquxgqf+x4vIbO7YK7aGpI10Ri7BZi7FaGdNO7/0SiqaN68NBFg7n/wsE8dvlQLhtR/zdSFfna7NDNrJVZOBrIx33c/vwy/jRnE09NG1Hr66f1TuGfPx7N099sw+n2cuO4Xozv35E3luytsp3Xa/Aaw9XpPfhozf6KmydP9KdLHtsnhevHpvHW0r0Y4IqR3Titd8PXCBrj+etG8fHaAyTHRXHB0BoZo1UESIqzc/s59V9YPVzg4M2le1m9N5deHeK4dXwfhnZLaqYWqubWZgN9fmnj5ho3NIQy5ZRUppxyInAaYzijbwqLdmRz/PPEYhHO+tt8rjutJ1/cNZ7M3FJG9mxP+7ioiv0e+dEQth0uIiMzl4/XHuDM/h25NIg9shi7lR+PTQva8dTJ8XoN+3JLSY6LIimueRbHbT1cyLSXl+F0e3B6fKu5v9h4iL9NHd6kmWKq5WuTgX7n0SI8jcyvbRUhK7eUnilxAe/TPi7KP9vBV5fLY3B5PLy3ch9rs/J4//ZxNW5QnplTyurMXBz+awfPzdve5ECfU1xOudtL16QYnZPfAi3blcNds9ZQ4vTg8RouHd6Vv101HHuIbzj/wEcbKK40FOk1vutRD3+8kQuGdiE2qnGpO1TL16YC/Zp9efzfJz+w6WAhjb0+tS+vlCnPfM8dE/tx95SBVV7bc6yEp/63lYU7jhFlFa4a3YNzBnbifz8cxuWpWVO528u2w8V8lLGf60/vVeW15LgoBF9QtlmEtEZ8sBx3rLicO99bw9rMfESgS7sY/nn9aE7toV/NW4qCMhe3vbWKUueJ+7h+sfEQaSlxNc6v2uSXOsnMKaVHciwdEgK/jlNc7mbTgcJaX7OIsHJvLufoHdMiTpu5GLt2Xx7Xv7qcH5oQ5MHXGy93e/nX97tZsTunojwrt5TL/rmYrzcdpqTcTV6pi7eXZ/LLmRk43J46j1fm8vDa4j01ypPi7Lx+czqjerbnvFNS+cc1Ixvd1lveWEnG3jycHi/lbi+ZuaX8+NXlFDpcjT6WCo3vth6h+ncsh8vL+6uyGtx3wbajjHviO254bQVnPfkdX2wI/DZ/DX2v0+99kanNBPp/frezYjjkZDhcHt5dkVnx/IVvd1Ba7qbySJDT7aWk3NPgtLZD+Y5ay8/s35GP7zyLl28YQ0p8VK3b1GXHkSJ2ZZfUmO7pNYavNh5q1LFU6EgdIbWhIbbd2cX89M1VlLk8FJW7cbi83PfBesqcdXcqKouPtjG8R1KttRtjAlogqFqfNhPojxTWHlSPi7Gd+KeofPPv6gyw62hJxfOFO7KpZXQmoG8NcdHBHwvNK3Vhq6Xx5W4vuSXao28pJg/pXKP7HGO3cP3p9V8o//V/1tboQAhCTkl5wHU/NW04iTE2ov3nvFV8dT81bXijU2ur1qHNjNFfMqIbO7OLa/Tq7VZheI8kbhrXmw37CzDGMLRbEkeLHDz/7Y5avwXsOFJEmdNDbJSVxGgbR6j5JhPAaqn7psp2qzBtdI+g/G2Vndo9qdY6o6wWJgzoGPT6VNO0i7Hz1k/HctestRXZJqeO6sHtZ9efiiCnuOa5FhdlJbVdTMB19++cyLf3TWTmikxW782jd4c4bj6zNwNSNcdSpGozgf7Ws/qwJjOP77dnYwy4vF6SYuxMG9ODe88fSFyUrcoy8SKHi7eXZXK0yEH1/FA2q3CooIy+nRK44YxePPm/bScWQPlF2YQO8dEcqWV/wTfNMcom3P/heob3SOKKUT04UuggM6eEUT2TSW7kkM1xsVFW/nLFMH7/yUZcbi8e4wsEl47oxrDuejG2JUnvncLSBydzuNBBuxg78QGkpb5jYn/+8sUWnG5f8rLu7WN5/Zb0Rs/U6ZQYzT0BXPRVkUECWR3anNLT083q1atDdvwD+WXkFJczpGu7Bt8cx4rL+dNnm5izoerYdozdQsYj5xEfbcPt8TL9nQyW786hzOnB6s8tcs+UAQxMTeDnb2dQvYNtEYi2WTD4LsDF2a1YLL4LvnarBbtV+PLuCXRNim3y37n9SBEfrM6ixOnh4mFdOat/B51iCYhIhjEmPRx1B+vc3rA/n325pYzr26FRM25UZKvv3A55oBcRG7Db/wNwlzFmY13b1/ZmKCl3M2/LEUqdHiYP7lzv11Sv1zc7JsZuCVpge2PJHp76ait2mwW3x/DstSO4cFjXiteNMazOzGP+1qPE2n295+7JsZz213nklzZ+XNxmEe47fxC/nKhpY4MtEgK9UrWp79xujqGb4cAsY8wDTdl52+EirnllGW6PF6+BP362iaenjeDSkVUXER0tdPCPudv5ZO0B3F5DfJSVG87oxV2TB5z0ApBbz+rDBUO7cCCvjD4d42tkkxQRTuudUiVdwTebDuNqRE7wyuxWC8nNtEpSKRX5mmPWzRnAJSKyUkRe9/fwA3bf7HUUlLkocXooc3kod3u5/6MNlDpPrOw7Wujg4hcW8VHGfsrdXjxeQ6HDzeuL9zDtX0txuAKbelaf7u1jGdsnpd6UwRv25zNzRSYb9xeQmVOKM8BcOpXZLMI5Aztx1ZjgX6htC/bnlTJj4S6OFtU/y0qptqQ5Av0qYIoxZixgBy6uvoGITBeR1SKyOjs7u6Lc7fGy6VDNVXw2i7Bxf0HF8ye+2kpeqbPGbJNyt5dd2cX8p5H3bm2KLzYc5JpXlvHnzzdzzSvLOJhfSlQjLpBZBc47JZWdj1/Mv24cE/Jl8JHqgY828MSXW/nz55tP+lgikioia/2PXxeRZSLySKXXa5Qp1RI1RzTZYIw5fjVzNTCg+gbGmBnGmHRjTHqnTieWX1stQnxUzS8ALq+Xzv5xeqfby+cbDtaY2XKcw+XlrWV7T/ZvaNCL/gVZDpeXMpeHpbtz651LbxUh1p/aOC7KSsfEaP58+bBat/V6Dbuzixudn6ctunBoV9rF2jl/SJdgHO5pIFZEpgJWY8w4oK+IDKitLBgVNoXb4+W1Rbu56PmFXDdjGQu3Zze8k2pTmmOM/h0R+SvwA3AF8HigO4oId07qxwvf7qyYvhhts3Ba75SKOyOVlLtx17ZiqZKc4sZlqmyKdjF2jqcvE6B9rJ17rx3J3f9ZS7nbW7HIxW4V4qJsvHPrWNbsy2NXdgmn9kji0uHd6ryW8Lf/beW1Rbu59rQ0nph6asj/ltbsxnG9uHFcr4Y3bICITAZKgMPARGC2/6VvgPHAqFrKdtRynOnAdIC0tNBkDX3ovxuZs+FgxZqPdVmreekno5k8WNNQK5/mCPSPAe/hi3+fGWPmNWbnX5zTj+S4KF5dtJsyp4crRnXn1+ee6DwlxtgQod50AyczTTFQf75iGNe+sgynx0uU1cKfrxjGwNREPvzFmbz43Q5W7M7FbrVwyYiu3H52P7okxTC8Z/uAju3yeLGI4Kwnd44KHhGJAv4AXAl8AsQDB/wv5wKj6yirwRgzA5gBvlk3wW5rQZmLT9cdrHIzcIfLy7Nzd5xUoN9yqJDvth5l2pgejVqMpVqmkAd6Y8wP+GbeNImIcN3YNK6rI4e6zWrhshHd+HTdwVqHSmLtVn42oU9Tqw/YoC6JLH/4XA4VOOiaFFOxlHxY9yReubHxs/nyS50s3nmM+GgbD180hKtG92BQF1252EweBF4yxuT7p+gWA8d7Cwn4hjxrK2t2RQ5Xrek6altB2xg3vLaCvFIni3ccY9b0M07qWCr8ImJl7J8uH8b6/QUczC+rcoPvGJuFtA6xRNksOFyekOfxiLFb673Zttvj5Tez1/HdlqOMSktmxk1jiKvlGsSRQgcXP78Ih8uDAcb2TuGNW07DUlcCHhVsU4DJInInMBJIA7KA5cAIYBuwH99wTeWyZte9fSydEqPJyiurKIuyWbh4eNd69grguMmxFJW76dWh8WmyVcsTEVM7kmLtzLlrPHdN7k9qu2hi7BZ6JsciImTllvHQfzdy8QuL6r3va3P4YuMhvt1ylBKnh5V7c3lr6d5at3tr6V7yy5yUOD2UOj0s353D/1uws8pMIxU6xpizjTETjTETgXX4gv2NIvIMcA3wBb4hneplzU5EeO3m00htF01clNV3DatXMveed3LpDWbfPo6P7ziTv16p14QiQUT06AESom38avIAfjXZN35/93/WMmf9wYr0AwfyyvhgdRa3nBX6YZy6OCtdlPUaU+XbR2UujxdvpZccbi//b/5OXpq/i+ln9+U3J/kmVoHzB3tEZCJwHvCUMaagrrJwGNQlkWUPnsu2I0UkRNsadRe0usTYrXoP2QgSET362uQWO6vkmHF5vBSUNT1Nr8dr+HTdAVbvrf/esfW5dEQ3TumWiEWgV0ocN43rXet2Px6bRny0jUqZkyumbf7r+11k5ZY2uQ2qaYwxecaY2caYw/WVhYLT7eXTdQe4490Mbn9nNR9m7K+xCNBiEYZ0bReUIK8iT8T06Ksb178Dy3fn4PJH+yib5aRmIcxauY+/fLEZY2Devec06Q0VY7fy0S/PavB6Qd9OCXx19wTeWrqXd5ZnVun5R9ksHCl06Bu6jTiQX8a0l5dS6HBRUu4L7ot3HONvX21h9u3j6NspIcwtVK1BRPbodx4t4p/f7cTtNQi+lKz/umFMQPdM/XbLEU776zwGPfIV17+6vCJXeGKM7zPRIlJxw4amCuSicM+UOO49fyBR1eryeA399M3dJhhjuPn1FRwtLK8I8gAlTg85JU5ueG2FLqJTAYnIQD9/azYuf75ug2/l6cRBnWtsd6y4nKJK91HdfLCQO99bQ3ZROeVuLyv35PLTf68E4PKR3Zn5s9P53z0TKlblhlpclI03fzqWpFg7dquQEG3jtZvSm5yrXrUuqzPzOFjgwFPLIhFjoMDh0lWwKiAROXTTp2M8dpsFl9OD3SL071yzB1zm9HD649+SlhLH/N9OBHy3Bay8ytbtNfxwsJBSp5u4KBtjetW8n6YxhtcW7WHLoULunjKAXh3qnl7ZFGN6JbPu/86joMxFuxi7TrFsQ9bty683A2pJuYc1mXlMGlyzE6NUZRHZoz93SGd+Nbk/vTvGMXFwZ56eNqLGNlE2CxP6d+ScgSdy6/gugFYNpAL1JidbsD2bZ+Zu5+O1B7hj5hocLg9vLtnDC9/uYH+e76JpmdPDve+v46LnF/LFhoNV9v/vmv0MffR/3PbWKrzVvobP33qUKc8s4Oy/z+fTdQeqBPmScjct7aYxKrii7Ras9dxTwWoRYk4yBbdqGyKyRy8i3DGxP3dM7F/nNlaL8OatY6uUXT6yGy/N38mx4nJcHkOs3crPJ/TFVk+gt1ss/jp9eeRvemMl67PycXt9iaa+vW8iby3dyxcbD1Hu9nLv7PWk906pWFb+wrc7KCn3sGh7Npm5pRULrjYdLOCXMzMq8pf87attpMRHc+mIbsxZf5Bfz1rLJcO78uL1ta68VxFgypBU/vrFljpft1uFC4YGJXmbinAR2aNvqnYxdr66ewI/n9CXq8f04OmrR3Dv+fXPWT+rfwf+eNkp3Hxmb56+ejhrMvP8OfF9F06X7c4ht9RZ8RVcBIorLdy6/vQ0BBjUpR09kk/k5Fm2K6fKhbYyl4dvNvlm8e3LLcVqEfbklATxr1ctTbf2sVw2oltFltPKYmwWzhnYqdZhSaWqi8ge/cnYeKCA1xbtIT7a2mCQX5eVz21vrmJI13a85f92EGO3VgRyr4EeybH88px+zN96lCOFDq4a3YO+ldIkTD+7H7ec2Qe7Varc+rBTYjR2qwWXxzfbwm4Vuvs/CG4/uy+ndG3H8ABmEanW7YmppxIfZWPWqn0V9yhwebxcNrIbf76i9rTWSlWngb6a45kALS5YuSeXy0d2r3Pbz9YdIKfEyfLdORzML6NnShxv/vQ07v7POorL3dwxqR+j05IBWPrgZDxeU+swUPUplACXDO/Gx2sOsGJPLhaBru1juWOSbyjKZrXoBbg2wma18MfLh/Kb8weyak8uXmNI751Cis68Uo3Q5gO9w+XhQH4ZfTvGIyLccmZvFu3IpkN8NOm9Uipm3FSWV+Kk1OXhxnG9ycjMY3iP9hXDLum9U1jy4OQa9YgINmvgM2asFuHfPz2NLYeKcHq8nNK1Xa0fCKptSIq1M+WUyM8vf9/sdXy39Sjv3HY6w7rrN9ZgaZOBvqTczcq9uYxJa89Fzy/mWHE5U0d354mpwxnWPYkVD0/hpQU7mfj0fAThqWnDuWKUr2e/K7uYS19cjMdreHraCD791fiQtVNEOKVbu5AdX6nKDhc4+Nf3uxjTqz2Xjqj7m2wofbf1KIUON+uy8jXQB1GbDPT3fbCeeZuPcOGwLhwpLMPthcU7j1W87nR7eeab7f570Br+OGdTRaDfdLAQYwxOt5fFu45x6chuYforlKpbTnE5NquFpFh7wPv87sP1LNl5jFkrLZzSLSksK7Dfue101mXlc3V6j2avO5K1yUCflhKH22tYn5WPxwtd2kXzlytOpGO1WnzDLMdvNp4QfeKf6bwhqcwZ0ImjRQ7umNiv2duuVEOycks579nvibJaWPLgZBJjAgv2yXFRRNksCL7V5OEwrHuS9uRDoE0G+ocuGsydk/pzw2srOFTg4O4pA6ssnLJahFdvSufBjzYSbbfwwnWjKl6LjbLy6k2Nv2OUUs3F5fGlw3Z5TJV01w15atpwJm7sxKAuic1y+03VfKSlra5MT083q1evbpa6isvd7MkuYVj3dlWmNqrIJSIZxpiwfFI357m991gJ0XaLBuw2pL5zu0326I9LiLYFlNFSqdamdz23tFRtj87XU0qpCNdsgV5EXheRZSLySHPVqZRSqpkCvYhMBazGmHFAXxEZ0Bz1KqWUar4e/URgtv/xN0CVVUYiMl1EVovI6uxsvZGCUkoFU3MF+njggP9xLlBlLbcxZoYxJt0Yk96pU6caOyullGq65gr0xcDxeV4JzVivUkq1ec0VcDM4MVwzAtjbTPUqpVSb1ywLpkSkHbAI+Ba4CDjDGFNQx7bZQGYQq+8IHGtwq/DSNgZHIG3sZYwJy/hgkM/tSPn/aAlaQztP6txutpWxIpIMnAcsNMYcbpZKffWuDtdKyEBpG4OjNbQxWFrD39oa2gito50n28ZmWxlrjMnjxMwbpZRSzUQviiqlVIRrC4F+RrgbEABtY3C0hjYGS2v4W1tDG6F1tPOk2tjislcqpZQKrrbQo1dKqTZNA71SSkW4iAr0IpIkIl+JyDci8rGIRLXUrJkikioia/2PW2obXxKRS/2PW1QbRSRZRL7050h6xV/WotoYTHpuB1dbO7cjKtADPwGeMcacDxwGrqPlZs18GohtqZk9RWQC0MUYM6eFtvFGYKZ/bnGiiNxPy2tjMOm5HSRt8dyOqEBvjHnJGDPX/7QTcAP1ZM0MFxGZDJTge8NOpIW1UUTswKvAXhG5nBbYRiAHGCYi7YGeQB9aXhuDRs/t4Gir53ZEBfrjRGQckAxkUU/WzHAQkSjgD8CD/qJ6M3uGyU3AZuApYCxwJy2vjYuBXsCvgS1AFC2vjUGn5/ZJa5PndsQFehFJAV4EbqVlZs18EHjJGJPvf94S2zgKmOFPVfEusJCW18ZHgV8YYx4DtgLX0/LaGFR6bgdFmzy3W8IfFTT+HsUHwEPGmExaZtbMKcCdIrIAGAlcSstr406gr/9xOtCbltfGZOBUEbECpwN/o+W1MWj03A6atnluG2Mi5gf4JZAHLPD/3AysB57B9xUoKdxtrNbeBUC7ltZGIBFfUFkILMP3NbKltXEssAlfr3FuS/x3DPLfq+d2cNrVJs/tiF8ZG66smY2hbQyO1tDGYGoNf6+2MThOto0RH+iVUqqti6gxeqWUUjVpoFdKqQingV4ppSKcBnqllIpwGuiDwJ9w6jsRWSAiVwbheM9VevxHEZlYx3YLTrYupeqj53ZkaLZ7xka4EcBSY0xQst8ZY+4JxnGUCgI9tyOA9uhPkojcDbwA3ODv9aSKyCwR+V5EZvpXNOJ/7e8i8rX/eYyI/EdEFovI5yISV+mYC6pVc6//eP/xr5arrR0iIq/6t/uwru0qteU3IrLB/9wqIu+JyBIR+VRE7CJyi4jM9/98LSJJ/m3/5G9zRZmKTHpuRw4N9CfJGPM8cA/wpjFmInA18IMx5hxgB768JABnAMuMMRf4n08H1htjxgMfAcPqqWa1/3gF+JaV1+ZywO7fbh/wo3qO19XXdDPc/7wD8AVwDlAIjPaX7zDGTALmAT8TkZHA2f42f4FvdaaKUHpuRw4N9MF3CrDC/3g5MMT/+AdjzH8rbTcYWOl//Cawqp5jHj/eGqBfHdsMAsb5e0xnU3+GuwJ8PbXjXMAl+JaG9+VEAqUM/+8N+HKCDMKXD3sBvkRLHeqpQ0UePbdbKQ30wbcJXw8H/+9N/sfF1bbbCpzmf/ww8LN6jjnG/3s4dSc02gb8x9/zugdfKta6lBpjvJWeTwV+8P8+UKl8rP/3KHzJoLYBC/x1/IwTb2bVNui53Urpxdjgew14U0QW4ssZ/ngd270KvOXvQeTgu4NQXSaIyPdANvBpHdt8BvzIv53Bd2OKQC3Bd2ODi4EyoLu/vKeIzAccwDXGmCIRyfLXEQXc3og6VOun53YrpbluVK1E5BYAY8yb4W2JUsHVFs9tDfQRTET+A3SpVnyRMaYsHO1RKlj03G4cDfRKKRXh9GKsUkpFOA30SikV4TTQK6VUhNNAr5RSEe7/A4Z8oqkixk0qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def THREE():\n",
    "    crime = pd.read_csv('crime.csv').loc[1:]\n",
    "    fig,axes = plt.subplots(1,2)\n",
    "    crime.plot.scatter(x='forcible_rape',y = 'murder',s = crime['population']//300000,ax = axes[0])\n",
    "    crime.plot.scatter(x='forcible_rape',y = 'burglary',s = crime['population']//300000,ax = axes[1])\n",
    "    plt.show()\n",
    "THREE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n",
      "c    6\n",
      "d    7\n",
      "e    8\n",
      "dtype: int64\n",
      "c    6\n",
      "d    7\n",
      "e    8\n",
      "dtype: int64\n",
      "6.5\n",
      "6.5\n",
      "4\n",
      "6\n",
      "8\n",
      "a    2\n",
      "b    5\n",
      "c    2\n",
      "d    7\n",
      "e    2\n",
      "f    9\n",
      "dtype: int64\n",
      "a     8\n",
      "b     5\n",
      "c    12\n",
      "d     7\n",
      "e    16\n",
      "f     9\n",
      "dtype: int64\n",
      "a     2.0\n",
      "b    10.0\n",
      "c     3.0\n",
      "d    14.0\n",
      "e     4.0\n",
      "f    18.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ser = pd.Series([4,5,6,7,8,9],index=['a','b','c','d','e','f'])\n",
    "print(ser['b'])\n",
    "print(ser[1])\n",
    "print(ser[2:5])\n",
    "print(ser[['c','d','e']])\n",
    "print(ser.mean())\n",
    "print(ser.median())\n",
    "for i in ser:\n",
    "    if i%2 == 0:\n",
    "        print(i)\n",
    "def func(x):\n",
    "    if x%2 == 0:\n",
    "        return 2\n",
    "    else:\n",
    "        return x\n",
    "print(ser.apply(func))\n",
    "def func(x):\n",
    "    if x%2 == 0:\n",
    "        return x*2\n",
    "    else:\n",
    "        return x\n",
    "c = ser.apply(func)\n",
    "print(c)\n",
    "def func(x):\n",
    "    if x%2 == 0:\n",
    "        return x/2\n",
    "    else:\n",
    "        return x*2\n",
    "print(ser.apply(func))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.0\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"weather.csv\")\n",
    "df1 = df[df['temperature'] < 99990]\n",
    "df2 = df1.groupby('city')['temperature'].max()\n",
    "print(df2['B'])\n",
    "def func(r):\n",
    "    r['rain'] = r['rain20'] + r['rain08']\n",
    "    return r\n",
    "df3 = df1.apply(func,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
